Sex Differences in Disease Genetics

Abstract

There is long‐standing evidence for the gene‐by‐sex interactions in disease risk, which can now be tested in genome‐wide association
studies with participant numbers in the hundreds of thousands. The current methods start with a separate test for each sex,
but a more powerful approach is to use sex as an interaction term in a combined sample. The most compelling evidence is for
adiposity (predictive of cardiac disease) as well as type II diabetes, asthma and inflammatory bowel disease. Autism exhibits a different kind of sex difference, with hypermasculinisation
of the brain, and the intriguing enrichment of structural variants in females. Sexually dimorphic gene expression varies exquisitely
and unexpectedly, by tissue, age and chromosome, so sex‐dependent genetic effects are expected for a wide range of diseases.
Because natural selection against sex‐dependent risk alleles is in one sex only, their effect size is expected to be greater
than conventional risk loci.

Key Concepts

Compelling findings of sex‐dependent genetic effects on disease have been made in adiposity‐related anthropometric traits,
type II diabetes and inflammatory bowel disease, and other disorders remain to be more fully investigated, regardless of what
sexual dimorphism they exhibit in prevalence and presentation.

Initial evidence indicates that sex difference in gene expression is not required for a sex‐dependent genetic effect on gene
expression. However, sex differences in expression levels vary dynamically by tissue type and age, so such generalisations
may be inaccurate, without more comprehensive data.

Sex‐dependent risk alleles are predicted to be of greater effect size than conventional ones, because natural selection acts
only against the sex which has the disease. There is evidence for this from a high‐powered GWAS of adiposity‐related traits.

Sexual dimorphism in gene expression seems likely to fall into different categories, and thus categorisation of sex‐dependent
genetic effects is expected to follow the same patterns.

Many of the large GWAS meta‐analyses look for sex‐dependent genetic effects by testing male and female groups separately.
However, this may be underpowered compared to a whole‐sample, gene‐by‐sex interaction test.

Figure 1. Disability‐adjusted life years (DALY) by age group and sex, for selected causes. Source data is from the global human population,
year 2012 (WHO, ). The causes were selected to show diverse and interesting patterns of sex differences. The arrangement of plots is by increasing
DALY from left to right, and vertically by sex difference. Note the y‐axis scale difference between plots, so that for Stroke (top‐left), the DALY is 1 million greater in middle‐aged men than
in women.

Figure 2. Male and female effect sizes of loci for waist‐hip ratio (adjusted by body‐mass index) in a genome‐wide association study
of 224 459 participants (Shungin et al., ). Loci which were calculated to have significant sex‐dependent effects are coloured; red for females and blue for males.